The assembly was created using only the diploid samples, and with the Yukon population removed (given its apparent distance from the rest of the sexual populations). SNPs were then called on the diploid reference using Freebayes, which is ploidy aware. This generated >400k SNPs, which were whittled down to ~16k SNPs using the following filtering steps:
Using poppr, we were able to identify the same number of clones (aka multilocus genotypes - MLGs) as the identity by state (IBS - from SNPRelate), but automatically and with the full ploidy information. One thing to note is that it grouped the sexual Yukon pop into the same MLG, which makes sense given that they had higher IBS values. The determination of MLGs in the apo’s should not be biased by the distance metric used (I’m using prevosti.dist(), which ranges from 0-1) as the within-population pairwise comparisons are always between individuals of the same ploidy. I picked 0.1 as the MLG cutoff. The number of MLGs remains stable from around 0.09 - 0.13.
## [1] 0.03889128
I also calculated pairwise distances within MLGs (apos) and populations (sexuals) and plotted them. The dashed line represents the mean distance between replicate pairs (as a proxy for error-rate), which falls right on top of our within-clone distance peak, which indicates to me that clones are pretty darned identical to each other. There’s also a nice separation between the apo and sexual peaks, which gives me confidence that there’s a clear separation between things that we’re calling “clones” and everything else. Note that there are a few red bars on the blue (apo) side of the plot…these are the sexual individuals from the Yukon population, which are most likely inbred / bottlenecked due to being disjunct from the rest of the sexual range for untold generations (*** they also have high levels of missingness, which probably contributes…we should remove the Yukon population?). The small blue hump at the same spot is from the “big MLG”, which may indicate that there are actually two (closely related) clones here…
It’s also worth noting that Meirmans et al (2018) report that this exact type of comparison (diploid-diploid vs polyploid-polyploid) is biased, so we should take these results with a grain of salt…
I used a K-means approach from the adegenet package (following this tutorial: http://grunwaldlab.github.io/Population_Genetics_in_R/clustering_plot.html), which uses a clustering algorithm that is similar to STRUCTURE. This is just looking at the apomictic individuals. Plot A below is based on 50 runs of the find.clusters algorithm, and shows that ~8-13 clusters have the lowest BIC. Plot B is a scatterplot of the discriminant functions (K=8), which helps us see how different the resulting clusters are. Plot C shows barplots of the posterior probability group assignments for K = 8-12, which helps visualize how the groups are assigned under different values of K.
A new paper from Patrick Meirmans’ group suggests that STRUCTURE is better than K-means and other clustering approaches for mixed-ploidy populations. However, this is only really a problem (for K-means) if you have low numbers of markers and missing dosage information…fortunately for us we have full dosage and tons of markers. My understanding is that DAPC is still better for us because it uses model-free methods, while STRUCTURE assumes panmixia and that markers are not linked.
These results are more-or-less congruent with the MLG designation based on genetic distance. When K=8, the BC and SK pops are grouped together, as well as the L62 apo + the one-off from L39 (these are different clones using genetic distance cutoffs). As K increases, it breaks up the big MLG (in blue), which could indicate that there are actually two closely related clones within that MLG (decreasing the distance cutoff does eventually split that MLG…).
This is a cool way to confirm ploidy using genomic data found here: https://knausb.github.io/vcfR_documentation/determining_ploidy_1.html. Basically, you can harness the ‘DP’ (sequencing depth) field from your VCF files to calculate the ratio of times each allele was sequenced in each individual. The idea is that if you sequence a diploid heterozygote (A/T) at 30x coverage, we would expect to sequence each allele around 15 times (giving an allele balance of 1/2). For a triploid (A/T/T), we’d expect to sequence the ‘A’ 10 times and the ‘T’ 20 times, giving allele balances of 1/3 and 2/3. If calculate the mean allele balances for each individual and plot them, you’d thus expect to see a single peak at 1/2 for diploids, two peaks (1/3 and 2/3) for triploids, and 3 peaks (1/4, 1/2, and 3/4) for tetraploids.
Here’s my new and improved DAPC:
zooming in a bit…